


'''simple docstring'''
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
  UpperCamelCase__          = argparse.ArgumentParser()

  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )
  parser.add_argument(
      '''--original_config_file''',
      type=str,
      required=True,
      help='''The YAML config file corresponding to the original architecture.''',
  )
  parser.add_argument(
      '''--num_in_channels''',
      default=None,
      type=int,
      help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
  )
  parser.add_argument(
      '''--image_size''',
      default=5_1_2,
      type=int,
      help=(
          '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
          ''' Base. Use 768 for Stable Diffusion v2.'''
      ),
  )
  parser.add_argument(
      '''--extract_ema''',
      action='''store_true''',
      help=(
          '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
          ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
          ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
      ),
  )
  parser.add_argument(
      '''--upcast_attention''',
      action='''store_true''',
      help=(
          '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
          ''' diffusion 2.1.'''
      ),
  )
  parser.add_argument(
      '''--from_safetensors''',
      action='''store_true''',
      help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
  )
  parser.add_argument(
      '''--to_safetensors''',
      action='''store_true''',
      help='''Whether to store pipeline in safetensors format or not.''',
  )
  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
  parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')





  def    a__			(   lowerCAmelCase__		)       ->      Any:
      if string == "True":
          return True
      elif string == "False":
          return False
      else:
          raise ValueError(F"""could not parse string as bool {string}"""		)

  parser.add_argument(
      '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
  )

  parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

  UpperCamelCase__          = parser.parse_args()

  UpperCamelCase__          = download_controlnet_from_original_ckpt(
      checkpoint_path=args.checkpoint_path,
      original_config_file=args.original_config_file,
      image_size=args.image_size,
      extract_ema=args.extract_ema,
      num_in_channels=args.num_in_channels,
      upcast_attention=args.upcast_attention,
      from_safetensors=args.from_safetensors,
      device=args.device,
      use_linear_projection=args.use_linear_projection,
      cross_attention_dim=args.cross_attention_dim,
  )







  controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




"""simple docstring"""



from collections.abc import Sequence
from queue import Queue







class    __a						:







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			start
					lowerCAmelCase_      							=			end
					lowerCAmelCase_      							=			val
					lowerCAmelCase_      							=			(start + end) // 2
					lowerCAmelCase_      							=			left
					lowerCAmelCase_      							=			right







				def __repr__( self   ):

					'''simple docstring'''






					return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""







class    __a						:







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			collection
					lowerCAmelCase_      							=			function
					if self.collection:
						lowerCAmelCase_      							=			self._build_tree(0      ,	len(UpperCAmelCase   ) - 1   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					self._update_tree(self.root      ,	UpperCAmelCase      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					return self._query_range(self.root      ,	UpperCAmelCase      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					if start == end:
						return SegmentTreeNode(UpperCAmelCase      ,	UpperCAmelCase      ,	self.collection[start]   )
					lowerCAmelCase_      							=			(start + end) // 2
					lowerCAmelCase_      							=			self._build_tree(UpperCAmelCase      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			self._build_tree(mid + 1      ,	UpperCAmelCase   )
					return SegmentTreeNode(UpperCAmelCase      ,	UpperCAmelCase      ,	self.fn(left.val      ,	right.val   )      ,	UpperCAmelCase      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					if node.start == i and node.end == i:
						lowerCAmelCase_      							=			val
						return
					if i <= node.mid:
						self._update_tree(node.left      ,	UpperCAmelCase      ,	UpperCAmelCase   )
					else:
						self._update_tree(node.right      ,	UpperCAmelCase      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			self.fn(node.left.val      ,	node.right.val   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					if node.start == i and node.end == j:
						return node.val

					if i <= node.mid:
						if j <= node.mid:
							# range in left child tree
							return self._query_range(node.left      ,	UpperCAmelCase      ,	UpperCAmelCase   )
						else:
							# range in left child tree and right child tree
							return self.fn(
							    self._query_range(node.left      ,	UpperCAmelCase      ,	node.mid   )      ,	self._query_range(node.right      ,	node.mid + 1      ,	UpperCAmelCase   )      ,	)
					else:
						# range in right child tree
						return self._query_range(node.right      ,	UpperCAmelCase      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.root is not None:
						lowerCAmelCase_      							=			Queue()
						queue.put(self.root   )
						while not queue.empty():
							lowerCAmelCase_      							=			queue.get()
							yield node

							if node.left is not None:
								queue.put(node.left   )

							if node.right is not None:
								queue.put(node.right   )


if __name__ == "__main__":
		import operator

		for fn in [operator.add, max, min]:
				print('*' * 50)
				lowercase_							=							SegmentTree([2, 1, 5, 3, 4], fn)
				for node in arr.traverse():
						print(node)
				print()

				arr.update(1, 5)
				for node in arr.traverse():
						print(node)
				print()

				print(arr.query_range(3, 4))  # 7
				print(arr.query_range(2, 2))  # 5
				print(arr.query_range(1, 3))  # 13
				print()